Methods and systems for understanding a meaning of a knowledge item using information associated with the knowledge item

ABSTRACT

Systems and methods that determine a meaning of a knowledge item using related information are described. In one aspect, a knowledge item is received, related information associated with the knowledge item is received, at least one related meaning based on the related information is determined, and a knowledge item meaning for the knowledge item based at least in part on the related meaning is determined. Several algorithms and types of related information useful in carrying out such systems and methods are described.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is a continuation-in-part of U.S. PatentApplication Ser. No. 09/493,701 filed Jan. 28, 2000 entitled“Meaning-Based Advertising and Relevance Determination,”which is is acontinuation-in-part of U.S. Pat. No. 6,453,315 filed Nov. 1, 1999entitled “Meaning-Based Information Organization and Retrieval,”whichclaims priority to U.S. Provisional Patent Application Ser. No.60/155,667 filed Sep. 22, 1999, all of which are hereby incorporated intheir entirety by this reference, and this application claims priorityto U.S. Provisional Patent Application Ser. No. 60/491,422filed Jul. 30,2003 entitled “Systems and Methods of Organizing and RetrievingInformation Based on Meaning,”which is hereby incorporated in itsentirety by this reference.

FIELD OF THE INVENTION

[0002] The invention generally relates to knowledge items. Moreparticularly, the invention relates to methods and systems forunderstanding meaning of knowledge items using information associatedwith the knowledge item.

BACKGROUND OF THE INVENTION

[0003] Two knowledge items are sometimes associated with each otherthrough manual or automated techniques. Knowledge items are anythingphysical or non-physical that can be represented through symbols and canbe, for example, keywords, nodes, categories, people, concepts,products, phrases, documents, and other units of knowledge. Knowledgeitems can take any form, for example, a single word, a term, a shortphrase, a document, or some other structured or unstructuredinformation. Documents include, for example, web pages of variousformats, such as HTML, XML, XHTML; Portable Document Format (PDF) files;and word processor and application program document files. For example,a knowledge item, such as, content from a document, can be matched toanother knowledge item, such as, a keyword or advertisement. Similarly,a knowledge item, such as, a document, may be associated with anotherdocument containing related content so that the two documents can beseen to be related.

[0004] One example of the use of knowledge items is in Internetadvertising. Internet advertising can take various forms. For example, apublisher of a website may allow advertising for a fee on its web pages.When the publisher desires to display an advertisement on a web page toa user, a facilitator can provide an advertisement to the publisher todisplay on the web page. The facilitator can select the advertisement bya variety of factors, such as demographic information about the user,the category of the web page, for example, sports or entertainment, orthe content of the web page. The facilitator can also match the contentof the web page to a knowledge item, such as a keyword, from a list ofkeywords. An advertisement associated with the matched keyword can thenbe displayed on the web page. A user may manipulate a mouse or anotherinput device and “click”on the advertisement to view a web page on theadvertiser's website that offers goods or services for sale.

[0005] In another example of Internet advertising, the actual matchedkeywords are displayed on a publisher's web page in a Related Links orsimilar section. Similar to the example above, the content of the webpage is matched to the one or more keywords, which are then displayed inthe Related Links section, for example. When a user clicks on aparticular keyword, the user can be directed to a search results pagethat may contain a mixture of advertisements and regular search results.Advertisers bid on the keyword to have their advertisements appear onsuch a search results page for the keyword. A user may manipulate amouse or another input device and “click”on the advertisement to view aweb page on the advertiser's website that offers goods or services forsale.

[0006] Advertisers desire that the content of the web page closelyrelate to the advertisement, because a user viewing the web page is morelikely to click on the advertisement and purchase the goods or servicesbeing offered if they are highly relevant to what the user is reading onthe web page. The publisher of the web page also wants the content ofthe advertisement to match the content of the web page, because thepublisher is often compensated if the user clicks on the advertisementand a mismatch could be offensive to either the advertiser or thepublisher in the case of sensitive content.

[0007] Various methods have been used to match keywords with content.Most of these methods have involved a form of text matching, forexample, matching the keywords with words contained in the content. Theproblem with text matching is that words can relate to multipleconcepts, which can lead to mismatching of content to keyword.

[0008] For example the term “apple”can relate to at least two concepts.Apple can refer to the fruit or the computer company by the same name.For example, a web page can contain a news story about Apple Computerand the most frequently used keyword on the web page, in this case“apple”, could be chosen to represent the web page. In this example, itis desirable to display an advertisement relating to Apple Computer andnot apple, the fruit. However, if the highest bidder on the keyword“apple”is a seller of apples and if the keyword “apple”is matched to theweb page, the advertisement about apples, the fruit, would be displayedon the web page dealing with Apple, the computer company. This isundesirable, because a reader of the web page about a computer companyis likely not also interested in purchasing apples.

[0009] Mismatching of knowledge items, such as keywords, to content canresult in irrelevant advertisements being displayed for content. It is,therefore, desirable to understand the meaning of knowledge items.

SUMMARY

[0010] Embodiments of the present invention comprise systems and methodsthat understand the meaning of knowledge items using relatedinformation. One aspect of an embodiment of the present inventioncomprises receiving a knowledge item and receiving related informationassociated with the knowledge item. Such related information may includea variety of information, such as, related documents and related data.Another aspect of an embodiment of the present invention comprisesdetermining at least one related meaning based on the relatedinformation and determining a meaning for the knowledge item based atleast in part on the related meaning of the related information. Avariety of algorithms using the related meaning may be applied in suchsystems and methods. Additional aspects of the present invention aredirected to computer systems and computer-readable media having featuresrelating to the foregoing aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] These and other features, aspects, and advantages of the presentinvention are better understood when the following Detailed Descriptionis read with reference to the accompanying drawings, wherein:

[0012]FIG. 1 illustrates a block diagram of a system in accordance withone embodiment of the present invention;

[0013]FIG. 2 illustrates a flow diagram of a method in accordance withone embodiment of the present invention; and

[0014]FIG. 3 illustrates a flow diagram of a subroutine of the methodshown in FIG. 2.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

[0015] The present invention comprises methods and systems forunderstanding the meaning of knowledge items using the knowledge itemitself as well as information associated with the knowledge item.Reference will now be made in detail to exemplary embodiments of theinvention as illustrated in the text and accompanying drawings. The samereference numbers are used throughout the drawings and the followingdescription to refer to the same or like parts.

[0016] Various systems in accordance with the present invention may beconstructed. FIG. 1 is a diagram illustrating an exemplary system inwhich exemplary embodiments of the present invention may operate. Thepresent invention may operate, and be embodied in, other systems aswell.

[0017] The system 100 shown in FIG. 1 includes multiple client devices102 a-n, server devices 104, 140 and a network 106. The network 106shown includes the Internet. In other embodiments, other networks, suchas an intranet may be used. Moreover, methods according to the presentinvention may operate in a single computer. The client devices 102 a-nshown each include a computer-readable medium, such as a random accessmemory (RAM) 108, in the embodiment shown coupled to a processor 110.The processor 110 executes a set of computer-executable programinstructions stored in memory 108. Such processors may include amicroprocessor, an ASIC, and state machines. Such processors include, ormay be in communication with, media, for example computer-readablemedia, which stores instructions that, when executed by the processor,cause the processor to perform the steps described herein. Embodimentsof computer-readable media include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the processor in communicationwith a touch-sensitive input device, with computer-readableinstructions. Other examples of suitable media include, but are notlimited to, a floppy disk, CD-ROM, magnetic disk, memory chip, ROM, RAM,an ASIC, a configured processor, all optical media, all magnetic tape orother magnetic media, or any other medium from which a computerprocessor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, private or public network, or othertransmission device or channel, both wired and wireless. Theinstructions may comprise code from any computer-programming language,including, for example, C, C++, C#, Visual Basic, Java, and JavaScript.

[0018] Client devices 102 a-n may also include a number of external orinternal devices such as a mouse, a CD-ROM, a keyboard, a display, orother input or output devices. Examples of client devices 102 a-n arepersonal computers, digital assistants, personal digital assistants,cellular phones, mobile phones, smart phones, pagers, digital tablets,laptop computers, a processor-based device and similar types of systemsand devices. In general, a client device 102 a-n may be any type ofprocessor-based platform connected to a network 106 and that interactswith one or more application programs. The client devices 102 a-n showninclude personal computers executing a browser application program suchas Internet Explorer™, version 6.0from Microsoft Corporation, NetscapeNavigator™, version 7.1 from Netscape Communications Corporation, andSafari™, version 1.0 rom Apple Computer. Through the client devices 102a-n, users 112 a-n can communicate over the network 106 with each otherand with other systems and devices coupled to the network 106.

[0019] As shown in FIG. 1, server devices 104, 140 are also coupled tothe network 106. The server device 104 shown includes a server executinga knowledge item engine application program. The server device 140 shownincludes a server executing a content engine application program.Similar to the client devices 102 a-n, the server devices 104, 140 showneach include a processor 116, 142 coupled to a computer readable memory118, 144. Server devices 104, 140 are depicted as a single computersystem, but may be implemented as a network of computer processors.Examples of server devices 104, 140 are servers, mainframe computers,networked computers, a processor-based device and similar types ofsystems and devices. Client processors 110 and server processors 116,142 can be any of a number of well known computer processors, such asprocessors from Intel Corporation of Santa Clara, California andMotorola Corporation of Schaumburg, Ill..

[0020] Memory 118 of the server device 104 contains a knowledge itemprocessor application program, also known as a knowledge item processor124. The knowledge item processor 124 determines a meaning for knowledgeitems. Meaning can be a representation of context and can be, forexample, a vector of weighed concepts or groups or clusters of words.The knowledge items can be received from other devices connected to thenetwork 106, such as, for example, the server device 140.

[0021] The knowledge item processor 124 may also match a knowledge item,such as a keyword, to an article, such as, a web page, located onanother device connected to the network 106. Articles include,documents, for example, web pages of various formats, such as, HTML,XML, XHTML, Portable Document Format (PDF) files, and word processor,database, and application program document files, audio, video, or anyother information of any type whatsoever made available on a network(such as the Internet), a personal computer, or other computing orstorage means. The embodiments described herein are described generallyin relation to documents, but embodiments may operate on any type ofarticle. Knowledge items are anything physical or non-physical that canbe represented through symbols and can be, for example, keywords, nodes,categories, people, concepts, products, phrases, documents, and otherunits of knowledge. Knowledge items can take any form, for example, asingle word, a term, a short phrase, a document, or some otherstructured or unstructured information. The embodiments described hereinare described generally in relation to keywords, but embodiments mayoperate on any type of knowledge item.

[0022] Memory 144 of server device 140 contains a content engineapplication program, also known as a content engine 146. In oneembodiment, the content engine 146 receives a matched keyword from theknowledge item engine 124 and associates a document, such as anadvertisement, with it. The advertisement is then sent to a requester'swebsite and placed in a frame on a web page, for example. In oneembodiment, the content engine 146 receives requests and returnscontent, such as advertisements, and matching is performed by anotherdevice.

[0023] The knowledge item engine 124 shown includes an informationlocator 134, an information processor 136, a knowledge item processor135 and a meaning processor 136. In the embodiment shown, each comprisescomputer code residing in the memory 118. The knowledge item processor135 receives a keyword and identifies known information about thekeyword. The known information may include, for example, one or moreconcepts associated with one or more terms parsed from the keyword. Aconcept can be defined using a cluster or set of words or termsassociated with it, where the words or terms can be, for example,synonyms. For example, the term ‘apple ’may have two concepts associatedwith it—fruit and computer company—and thus, each may have a cluster orset of related words or terms. A concept can also be defined by variousother information, such as, for example, relationships to relatedconcepts, the strength of relationships to related concepts, parts ofspeech, common usage, frequency of usage, the breadth of the concept andother statistics about concept usage in language.

[0024] The information locator 134 identifies and retrieves relatedinformation associated with keywords. In the embodiment shown, therelated information could include related documents and additionalrelated data. The related documents could include the text of theadvertisements and the destination web site from advertisers that havebid on a keyword. The additional related data could include otherkeywords purchased by the advertisers, search results on a keyword froma search engine, cost per click data on the advertisers, and datarelated to the success rate of the advertisements. Some of thisinformation can be obtained, for example, from the server device 140.The information processor 136 processes the related information locatedby the information locator 134 to determine at least one related meaningfor the located related information. This related meaning and the knowninformation about the keyword are then passed to the meaning processor137. The meaning processor 137 uses the known information about thekeyword and the related meaning to determine the meaning of the keyword.Note that other functions and characteristics of the information locator134, knowledge item processor 135, information processor 136, andmeaning processor 137 are further described below.

[0025] Server device 104 also provides access to other storage elements,such as a knowledge item storage element, in the example shown aknowledge item database 120. The knowledge item database can be used tostore knowledge items, such as keywords, and their associated meanings.Server device 140 also provides access to other storage elements, suchas a content storage element, in the example shown a content database148. The content database can be used to store information related toknowledge items, for example documents and other data related toknowledge items. Data storage elements may include any one orcombination of methods for storing data, including without limitation,arrays, hashtables, lists, and pairs. Other similar types of datastorage devices can be accessed by the server device 104.

[0026] It should be noted that the present invention may comprisesystems having different architecture than that which is shown inFIG. 1. For example, in some systems according to the present invention,the information locator 134 may not be part of the knowledge item engine124, and may carry out its operations offline. The system 100 shown inFIG. 1 is merely exemplary, and is used to explain the exemplary methodsshown in FIGS. 2-3.

[0027] Various methods in accordance with the present invention may becarried out. One exemplary method according to the present inventioncomprises receiving a knowledge item, receiving related informationassociated with the knowledge item, determining at least one relatedmeaning based on the related information, and determining a knowledgeitem meaning for the knowledge item based at least in part on therelated meaning of the related information. The related information maybe associated with the knowledge item in any way, and determined to berelated in any way. The related information may comprise relatedarticles and related data. Some examples of related articles comprise anadvertisement from an advertiser who has bid on a knowledge item and aweb page associated with the advertisement. The knowledge item can be,for example, a keyword. An example of related data comprises cost perclick data and success rate data associated with the advertisement. Inone embodiment, the knowledge item meaning may comprise a weightedvector of concepts or related clusters of words.

[0028] In one embodiment, the knowledge item is processed after it isreceived to determine any known associated concepts. A concept can bedefined by a cluster or group of words or terms. A concept can furtherbe defined by various other information, such as, for example,relationships to related concepts, the strength of relationships torelated concepts, parts of speech, common usage, frequency of usage, thebreadth of the concept and other statistics about concept usage inlanguage. In one embodiment, determining the knowledge item meaningcomprises determining which of the associated concepts represents theknowledge item meaning.

[0029] In one embodiment, the knowledge item comprises a plurality ofconcepts and the related meaning comprises a plurality of concepts anddetermining the knowledge item meaning comprises establishing aprobability for each knowledge item concept that the knowledge itemshould be resolved in part to the knowledge item concept, determining astrength of relationship between each knowledge item concept and eachrelated meaning concept, and adjusting the probability for eachknowledge item concept based on the strengths. In one embodiment, theknowledge item has a plurality of concepts and a plurality of relatedmeanings are determined, where each related meaning has a plurality ofconcepts. A knowledge item meaning determination involves establishing aprobability for each knowledge item concept that the knowledge itemshould be resolved in part to the knowledge item concept andestablishing a probability for each related meaning concept that theknowledge item should be resolved in part to the related meaningconcept.

[0030]FIGS. 2-3 illustrate an exemplary method 200 in accordance withthe present invention in detail. This exemplary method is provided byway of example, as there are a variety of ways to carry out methodsaccording to the present invention. The method 200 shown in FIG. 2 canbe executed or otherwise performed by any of various systems. The method200 is described below as carried out by the system 100 shown in FIG. 1by way of example, and various elements of the system 100 are referencedin explaining the example method of FIGS. 2-3. The method 200 shownprovides an understanding of the meaning of a keyword using informationassociated with the keyword.

[0031] Each block shown in FIGS. 2-3 represents one or more stepscarried out in the exemplary method 200. Referring to FIG. 2, in block202, the example method 200 begins. Block 202 is followed by block 204in which a keyword is received by the knowledge item engine 124. Thekeyword can for example, be received from an external database throughnetwork 106, such as the content database 148 or can be received fromother sources.

[0032] Next in block 206, the keyword is processed by knowledge itemprocessor 135 to determine known information about the keyword. Forexample, the keyword may have one or more concepts associated with it.Each concept may have an associated cluster or group of words. A conceptcan also be defined by various other information, such as, for example,relationships to related concepts, the strength of relationships torelated concepts, parts of speech, common usage, frequency of usage, thebreadth of the concept and other statistics about concept usage inlanguage.

[0033] For example, for the term apple there may be two possibleassociated concepts. The first concept of apple the fruit can be definedwith relationships to related words or concepts, such as, fruit, food,pie, and eat. The second concept of apple the computer company can bedefined with relationships to related words or concepts, such as,computer, PC, and technology. A keyword can be a short phrase, in whichcase, the phrase can be broken down by the knowledge item processor 135,for example, into individual terms. In such example, the knowledge itemprocessor 135 can further determine concepts associated with each term.In some embodiments, the keyword will not have any informationassociated with it.

[0034] Block 206 is followed by block 208 in which related informationassociated with the keyword is identified by the information locator 134and received by the information processor 136. The related informationcan include documents, such as, the text of advertisements anddestination websites from advertisers who have bid on a keyword, websearch results on the keyword itself, and related data, such as, otherkeywords bid on by the advertisers, the cost per click that theadvertisers associated with the keyword are paying, the number of timesa user has bought an item after clicking through an associatedadvertisement to an advertiser's website. This related information canbe located from a variety of sources, such as, for example, the serverdevice 140, the advertiser's websites, and search engines.

[0035] Block 208 is followed by block 210, in which the at least onerelated meaning is determined from the related information by theinformation processor 136. For example, for each individual relateddocument a meaning could be determined or an overall meaning for all ofthe documents could be determined. For example, if the documents includethe text of five advertisements associated with the keyword, a relatedmeaning for each advertisement could be determined or the meanings ofall five advertisements could be combined to provide an overall relatedmeaning. In one embodiment, documents are processed to determine avector of weighted concepts contained in the documents. The vector ofweighted concepts can represent the meaning of the document. Forexample, if the advertisement relates to selling Apple Computers, themeaning of such an advertisement may be fifty percent computers, thirtypercent Apple Computers and twenty percent sales. The related data canbe used, for example, to adjust the weights of the meanings ofindividual documents or of the overall related meaning. Alternatively,the meaning of a document could be related clusters of words.

[0036] Block 210 is followed by block 212, in which the meaning of thekeyword is determined based on the related meaning or meanings bymeaning processor 137. Meaning processor 137 receives the relatedmeaning or meanings from information processor 136 and the processedkeyword from knowledge item processor 135. For example, in block 212,the meaning processor would receive the keyword apple and its relatedtwo concepts from the knowledge item processor and would receive therelated meaning of the advertisement for Apple Computers from theinformation processor 136. A variety of methods could be used todetermine the meaning of the keyword based on the related meaning ormeanings received from the information processor 136. For example, therelated meaning can be used as a clue to determine the best concept toassociate with the keyword to provide a meaning for the keyword. Wherethe related meaning is, for example, fifty percent computer, thirtypercent Apple Computers and twenty percent sales the relationshipbetween the weighted concepts of the related meaning and the concepts ofthe keyword could be used to indicate that the keyword apple should beassociated with the concept of the computer company. Alternatively, therelated meaning or meanings and related data can be used to develop anew meaning for the keyword.

[0037] Any one or more of a variety of related information may be usedto determine the meaning of a keyword. The examples of relatedinformation that may be used to determine the meaning of a keywordinclude, without limitation, one or more of the following:

[0038] The text of advertisements associated with advertisers who havecurrently bid on the knowledge item.

[0039] The destination web page or web pages for the advertisements.

[0040] Text of advertisements from advertisers who have in the past bidon the keyword.

[0041] Other keywords bid on by the advertisers who currently have bidon the keyword.

[0042] Search results on the keyword from a search engine.

[0043] The number of people who have bought an item, after viewing theadvertisement, from an advertiser's website that is associated with thekeyword.

[0044] There are a variety of other related information that may beincluded, and these are only examples. Moreover, this relatedinformation may be given different weights depending on some of theinformation. For example, the text of advertisements of currentadvertisers may be weighted more than the text of advertisements offormer advertisers associated with the keyword. Further, the itemsassociated with the advertiser with the highest cost per click may beweighted more based on the cost per click.

[0045]FIG. 3 illustrates an example of a subroutine 212 for carrying outthe method 200 shown in FIG. 2. The subroutine 212 determines themeaning of the keyword using a related meaning or related meanings. Anexample of subroutine 212 is as follows.

[0046] The subroutine begins at block 300. At block 300, probabilitiesfor each set of words associated with the keyword are established. Forexample, in one embodiment each keyword can comprise one or more termsand each term can have one or more concepts associated with it. Forpurposes of this example, the keyword comprises a single term with atleast two related concepts. In block 300, each concept associated withthe keyword is given an a priori probability of the keyword beingresolved to it. This a priori probability can be based on informationcontained in a network of interconnected concepts and/or on previouslycollected data on the frequency of each term being resolved to theconcept.

[0047] Block 300 is followed by block 302, in which the strength of therelationship is determined between the keyword concepts and the relatedmeaning or meanings concepts. For example, in one embodiment the relatedmeaning may be comprised of a weighed set of concepts. A strength isdetermined for the relationship between each keyword concept and eachrelated meaning concept. The weight of each related meaning concept canbe used to adjust the strength of the relationship between the relatedmeaning concepts and the keyword concept. The strength can reflect theprobability of co-occurrence between concepts, or some measure ofcloseness of the two concepts, which can be derived from ontologicaldata.

[0048] Block 302 is followed by block 304, in which the strengthscomputed in block 302 are used to adjust the probability of the keywordbeing resolved to each of its associated concepts. For example, thestrengths determined for the relationship between each keyword conceptand each related meaning concept are used to adjust the probability ofeach keyword concept being considered. In one embodiment, after theprobabilities for the keyword concepts have been adjusted, theprobabilities are normalized to one. The steps occurring in blocks 302and 304 can be repeated a number of times to boost the impact of thestrengths of the relationships on the probabilities.

[0049] In one embodiment, the keyword can comprise multiple concepts andmultiple related meanings may each comprise multiple concepts. In thisembodiment, the keyword meaning can be determined by establishing aprobability for each keyword concept that the keyword should be resolvedin part to the keyword concept and a probability for each relatedmeaning concept that the keyword should be resolved in part to therelated meaning concept. These probabilities can be established in themanner described above with respect to FIG. 3.

[0050] Returning now to FIG. 2, block 212 is followed by block 214 inwhich the meaning of the keyword is associated with the keyword andstored. The keyword and its associated meaning could be stored together,for example, in the knowledge item database 120, or could be storedseparately in separate databases.

[0051] While the above description contains many specifics, thesespecifics should not be construed as limitations on the scope of theinvention, but merely as exemplifications of the disclosed embodiments.Those skilled in the art will envision many other possible variationsthat are within the scope of the invention.

That which is claimed:
 1. A method, comprising: receiving a knowledgeitem; receiving related information associated with the knowledge item;determining at least one related meaning based on the relatedinformation; and determining a knowledge item meaning for the knowledgeitem based at least in part on the related meaning.
 2. The method ofclaim 1, wherein the knowledge item is a keyword.
 3. The method of claim1, wherein the related information comprises related articles.
 4. Themethod of claim 3, wherein the articles comprise an advertisement froman advertiser who has bid on the knowledge item.
 5. The method of claim4, wherein the articles further comprise a web page associated with theadvertisement.
 6. The method of claim 5, wherein the related informationfurther comprises related data.
 7. The method of claim 6, wherein therelated data comprises cost per click data associated with theadvertisement.
 8. The method of claim 1, wherein receiving the knowledgeitem further comprises processing the knowledge item to determine anyknown associated concepts.
 9. The method of claim 1, wherein theknowledge item comprises a plurality of associated concepts anddetermining the knowledge item meaning comprises determining which ofthe associated concepts represents the knowledge item meaning.
 10. Themethod of claim 1, wherein the knowledge item comprises a plurality ofconcepts and the related meaning comprises a plurality of concepts anddetermining the knowledge item meaning comprises: establishing aprobability for each knowledge item concept that the knowledge itemshould be resolved to the knowledge item concept; determining a strengthof relationship between each knowledge item concept and each relatedmeaning concept; and adjusting the probability for each knowledge itemconcept based on the strengths.
 11. The method of claim 1, wherein theknowledge item meaning comprises a weighted vector of concepts.
 12. Themethod of claim 1, wherein the knowledge item meaning comprises relatedclusters of words.
 13. The method of claim 1, wherein the knowledge itemcomprises a plurality of concepts, a plurality of related meanings aredetermined, each related meaning comprising a plurality of concepts, anddetermining the knowledge item meaning comprises: establishing aprobability for each knowledge item concept that the knowledge itemshould be resolved in part to the knowledge item concept; andestablishing a probability for each related meaning concept that theknowledge item should be resolved in part to the related meaningconcept.
 14. A computer-readable medium containing program code,comprising: program code for receiving a knowledge item; program codefor receiving related information associated with the knowledge item;program code for determining at least one related meaning based on therelated information; and program code for determining a knowledge itemmeaning for the knowledge item based at least in part on the relatedmeaning.
 15. The computer-readable medium of claim 14, wherein theknowledge item is a keyword.
 16. The computer-readable medium of claim14, wherein the related information comprises related articles.
 17. Thecomputer-readable medium of claim 16, wherein the articles comprise anadvertisement from an advertiser who has bid on the knowledge item. 18.The computer-readable medium of claim 17, wherein the articles furthercomprise a web page associated with the advertisement.
 19. Thecomputer-readable medium of claim 18, wherein the related informationfurther comprises related data.
 20. The computer-readable medium ofclaim 19, wherein the related data comprises cost per click dataassociated with the advertisement.
 21. The computer-readable medium ofclaim 14, wherein program code for receiving the knowledge item furthercomprises program code for processing the knowledge item to determineany known associated concepts.
 22. The computer-readable medium of claim14, wherein the knowledge item comprises a plurality of associatedconcepts and program code for determining the knowledge item meaningcomprises program code for determining which of the associated conceptsrepresents the knowledge item meaning.
 23. The computer-readable mediumof claim 14, wherein the knowledge item comprises a plurality ofconcepts and the related meaning comprises a plurality of concepts anddetermining the knowledge item meaning comprises: program code forestablishing a probability for each knowledge item concept that theknowledge item should be resolved to the knowledge item concept; programcode for determining a strength of relationship between each knowledgeitem concept and each related meaning concept; and program code foradjusting the probability for each knowledge item concept based on thestrengths.
 24. The computer-readable medium of claim 14, wherein theknowledge item meaning comprises a weighted vector of concepts.
 25. Thecomputer-readable medium of claim 14, wherein the knowledge item meaningcomprises related clusters of words.
 26. The computer-readable medium ofclaim 14, wherein the knowledge item comprises a plurality of concepts,a plurality of related meanings are determined, each related meaningcomprising a plurality of concepts, and determining the knowledge itemmeaning comprises: program code for establishing a probability for eachknowledge item concept that the knowledge item should be resolved inpart to the knowledge item concept; and program code for establishing aprobability for each related meaning concept that the knowledge itemshould be resolved in part to the related meaning concept.